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@karpathy
karpathy / llm-wiki.md
Created April 4, 2026 16:25
llm-wiki

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@NeroxTGC
NeroxTGC / Tutorial.md
Last active May 7, 2026 08:36
100% SEO Score with OpenSaaS Template
@HDRobotica
HDRobotica / gist:b0418fc0393713ee0247296dacedbc56
Last active May 7, 2026 08:34
Smartwatch mit ESP32-C3 & OLED
#include <WiFi.h>
#include <Wire.h>
#include <Adafruit_GFX.h>
#include <Adafruit_SSD1306.h>
#include <NTPClient.h>
#include <TimeLib.h>
#include <Preferences.h>
#include <HTTPClient.h>
#include <esp_sleep.h> // Für Deep Sleep
@ladymeyy
ladymeyy / gist:4d4b92e3b38bc12dac5e712a09a5e59f
Last active May 7, 2026 08:34
Create your own KIPI
> You are Kipi Setup Assistant.
>
> Greet the user warmly and introduce yourself:
> "Hi! I'm going to help you set up Kipi — your Core-Knowledge Keeper. Kipi is an AI agent that builds and maintains deep, structured documentation for your project so that anyone — even someone who has never seen the codebase — can understand, fix, or rebuild it."
>
> Then, check for BMAD:
> Look for a _bmad/ directory in the project root.
> - If found: Say "I see you already have BMAD installed — great! I'll use the BMAD framework to build Kipi with full compliance."
> - If not found: Say:
> "Before we continue, I noticed you don't have the BMAD Method installed in this project. I strongly recommend setting it up first — BMAD provides the framework that Kipi is built on, including structured agent patterns, menu systems, workflows, and compliance validation. It will make Kipi much more robust and maintainable.
@DocShotgun
DocShotgun / llamacpp-moe-offload-guide.md
Last active May 7, 2026 08:29
Guide to optimizing inference performance of large MoE models across CPU+GPU using llama.cpp and its derivatives

Performant local mixture-of-experts CPU inference with GPU acceleration in llama.cpp

Introduction

So you want to try one of those fancy huge mixture-of-experts (MoE) models locally? Well, whether you've got a gaming PC or a large multi-GPU workstation, we've got you covered. As long as you've downloaded enough RAM beforehand.

Anatomy of a MoE Model

MoE models are described in terms of their total parameters and active parameters - i.e. DeepSeek V3 671B A37B has 671B total parameters, but we are using only 37B parameters at a time during each forward pass through the model.

@rohitg00
rohitg00 / llm-wiki.md
Last active May 7, 2026 08:28 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.